Risk Assessment

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Aim of the exercise:

In this exercise, you will go through the different steps requiring QGIS in order to conduct a spatial risk assessment.

Background

In the context of the Forecast based Financing methodology the conduction of a robust risk assessment is a crucial step towards the development of an Early Action Protocol. A risk analysis serves to understand what kinds of disaster impacts can be expected from a particular type of hazard and to identify who and what is exposed and vulnerable to this hazard and why. By overlaying the information on exposure, vulnerability and lack of coping capacity, it will become clear which areas are predicted to be most severely impacted. These areas can then be targeted as priority areas for early action to ensure the most at-risk communities receive assistance before the event happens. The collection and processing of this information varies throughout different contexts but the calculation scheme to combine the information to a risk score remains consistent.

Somalia is heaviliy exposed to droughts. We will showcase a risk assessment for a drought Early Action protocol.

Part 1: Indicator Processing

Task

The first part of the exercise will prepare the data in order to serve as indicator values. Raw data will be processed into meaningful indicators, and the vulnerability index will be calculated. Finally, all risk relevant data will be joined into a single vector layer using spatial data geoprocessing.

Data

Download the data folder for “Modul_5_Exercise2_Drought_Monitoring_Trigger.zip” Here. In the folder, you can find two folders. One for the first part (“Modul_5_Ex1_Part_1”) of the exercise and one for the second part (“Modul_5_Ex1_Part_2”).

Download the data folder for the first part of the exercise: “Modul_5_Ex1_Part_1”.

Download all datasets and save the folder on your computer and unzip the file. The zip folder includes:

• som_admbnda_adm2_ocha_20230308.shp: Somalia district boundaries (Admin level 2)

• WHO_health_sites.shp: Healthsites Somalia

• som_ppp_2020_UNadj_constrained.tif : Worldpop Population Counts Somalia

• Somalia_malnutrion_district_2022_2023.xlsx:Somalia: Acute Malnutrition

Hint

All files still have their original names. However, feel free to modify their names if necessary to identify them more easily.

1. Load the Somalia district boundaries (admin level 2) (som_admbnda_adm2_ocha_20230308.shp) and the Healhsites Somalia (`WHO_health_sites.shp) into QGIS.

2. Make sure to that both datasets are in the same projection. In this case we have two different projections and we will reproject the Healthsites Somalia into EPSG 4326. Use the tool Reproject layer for this process. See the Wiki entry on Projections for further information.

Attention

Before you start doing any GIS operations, always explore the data. Always check if the projections of the different layers are the same.

3. In order to bring the information of the healthsites point layer into a usable indicator value we can now count the healthsites per district. We can use the tool Count points in polygon from the Processing Toolbox. Take a look on the tools description and the further features it brings. For our task we only have to specify polygon and point input layer, the count field name (e.g. Num_healtsites) and choose name and directory of the output layer. Explore the output.

Fig. 143 Count healthsites per district

4. Now we have the number of healthsites per district. Nevertheless, it would be interesting to know how many healthsites exist per 10.000 people. For this task we firstly need to know how many inhabitants has each district. We can proces this information by using the Zonal statistics tool from the Processing Toolbox. See the Wiki entry for Zonal Statistics for further information. Specify your inout layer (Output of step 3 e.g. Num_healtsites) and your raster layer (Worldpop Raster), specify the column prefix (e.g. _wpop) and select the statistics to caclulate (sum). For each distrcit all pixel values of the Worldpop Raster that fall inside of it will be summed up. Explore the output.

Fig. 144 Summarize population counts per district

Hint

Throughout the indicator processing process you will have several interim results. Make sure to save them in a “temp” folder.

5. Now we know the numbers of healthsites and the number of population per district. We are ready to caclulate our final indicator __Number of healthsites per 10.000 inhabitants.

• Open the attribute table of “Num_healthsites_wpop” (Output of step 4) and Open the Field Calculator by clicking on the button . By checkin the box for Create a new field we can conduct calculation and saving them right away in a new attribute column.

• define the output field name as “healthsites_10000” and set the Type to Decimal Number(real).

• Now we will caclulate in the expression field the number of healthsites per 10.000 inhabitants:

("Num_healthsites"/"_wpopsum")*10000

• When you are down click to save your edits and switch off the editing mode by again clicking on (Wiki Video).

Fig. 145 Calculate healthsites per 10000 inhabitants

6. Land Degradation

A very important factor of areas vulnerable to drought is the siuation with regards to land degradation. It is an important factor not only for agriculture but also livestock herds, both as main sources of livelihood. We will try to add this information to our dataset:

-Somalia Land degradation

Fig. 146 Land Degradation

You will see that we can only download the information as image. This is a very common case when working with open data. We would have to digitize the information in order to be able to use it for further processing. Find the digitized version in “Modul_5_Ex1_Part_1\land_degradation_somalia”.

Explore the data. We have a column “LandD_CLas” which indicators the severity of land degradation from 0 to 3. We now want to join the respective land degradation class to its belongig district by considering the largest overlapping area.

• Open the tool Join attributes by location from the Processing Toolbox.

• define Input Layer (layer you want to enrich) and Join Layer (dataset with the additional information)

• select ìnteresects as geometric predicate and add only the LandD_class as field to add to our base layer.

• as Join Type set Take attributes of the feature with largest overlap only (one-to-one)

• Save as Layer

See the Wiki entry Spatial Joins for further information.

Fig. 147 Join Attributes by Location

7. Malnutrition

Another very important indicator to describe vulnerability in a district is the acute malnutirion, especially in children under 5 years old.

Dowload the data we need: Somalia_malnutrion_district_2022_2023.xlsx:Somalia: Acute Malnutrition

Explore the data. In which resolution is the data avalaible? Do you have ideas how we can add it to our indicator dataset?

*Save the Excel file as csv by clicking on Save file as and choosing csv (delimiter-separated)

• Load the csv-file into you QGIS by drag and drop

• Open the tool Join attributes by field value from the Processing Toolbox

• specify our two datasets we want to join as well as the common field available for joining (ADM2_EN and adm2name)

• as join type set Take attributes of the first matching feature only (one-to-one)

• Save the Layer to File

Fig. 148 Join attributes by field value

In the Log file you will get a message: “6 feature(s) from input layer could not be matched”

Attention

It can occur, that after the import of the csv. file the column headers of the attribute table are not correctly named (instead with “field 1”, “field 2” etc.). The information which field is displayed can be located below the header in this case.

Fig. 149 Log File Join Attribute by Field Value

Open the attribute tables from both, your output layer and the csv file in order to find out the roots of the problem. In the output layer doble-click on affunderfive and bring NULL Values to the top. Check the joining attribute “ADM2_EN” and compare it with the joining attribute “adm2name” from the csv-file. The Spelling of the distrcit anmes differs. Since we take the admin-2-boundaries as base layer for all indicators we can now adapt the names in the csv file. You can edit the names in 6 cases in the Attribute Table of the csv-file by toggling editing mode, or you can simply edit the csv file in excel an load it again into QGIS.

• Make sure to call your final layer “vulnerability_districts”.

Part 2: Risk Caclulation

Task

In the second part of the exercise we will showcase the steps how to come from indicators to a risk analysis.

You can find all the data for the second part of the exercise in the “Modul_5_Ex1_Part_2”. Download the data folder for the second part of the exercise: “Modul_5_Ex1_Part_2”. We processed the vulnerabilty layer in the first part of the exercise; simplified exposure and lack of coping capacity layers have been prepared in advance for this exercise. These layers have only 3 to 4 indicators for complexity reasons. See below an example of indicators that were used for Somnlia:

Fig. 150 Indicators Risk Assessment

1. Normalization

For further caclulations with the indicators we need to make them comparable. For this reason we normalize them to a range of 0-1.

\( Normalized\ Value\ = \frac{value\ -\ min value}{max\ value \ - \ min } \)

• Open the attribute table of “vulnerability_districts” and Open the Field Calculator by clicking on the button . By checkin the box for Create a new field we can conduct calculation and saving them right away in a new attribute column.

• start with the first indicator LandD_class

• define the output field name as “LandD_class_norm” and set the Type to Decimal Number(real).

• Now we will caclulate in the expression field the normalization of the indicator:

("LandD_Clas" - minimum( "LandD_Clas" ))/( maximum( "LandD_Clas") - minimum( "LandD_Clas" ))

• When you are done click to save your edits and switch off the editing mode by again clicking on (Wiki Video).

Fig. 151 Normalization of indicators

• Repeat this step for the other indicators

• For each indicator you have now the original column and the normalized column.

2. Directions

The direction indicates if an indicator follows the predefined logic: “the higher the value, the worse the circumstances” meaning that higher values would result in a higher risk. The logic is adapted for all three dimensions, since it is generally logical to think about high values = high risk. If a respective indicator follows the logic the direction would be 1, if it does not, the direction would be = -1.

• In order to understand the directions of our indicators we first have to assign them to one of the dimensions (exposure, vulnerability, coping capacity).

• To which dimension would you assign the processed indicators and what are their direction?

Depending on the direction the next step will vary:

If the direction is 1 (indicating a positive weight), the formula is straightforward:

\( weighted= value \times weight \)

If the direction is -1 (indicating a negative weight), the formula adjusts the value by subtracting it from 1 before applying the weight:

\( weighted= (1 - value) \times weight \)

The second formula inverts the value \((1 - value)\) before applying the weight, resulting in a different calculation for variables with negative weights.

We will not go further into this in this training but you can find more information here.

Hint

It is recommended to properly check the logic of each indicator. Often the indicators of a certain dimension follow the same logic but there are always exceptions. After the directions have been applied to the data, we can speak of “lack of coping capacity” instead of “coping capacity” since we force the respective indicators in another direction following the predefined overarching logic (the higher the value = the worse the circumstances).

3. Weighting of Indicators

In the next step we can weight the indicators based on their relevance to our risk assessment. It is recommended to do surveys or workshops with the local staff in oder to find out the importance of the respective indicators.

We have used so far the following weighting scale:

Weight	Definition
0	Not Important
0.25	Slightly Important
0.5	Moderately Important
0.75	Fairly Important
1	Very Important

• In the attribute table of your layer we can calculate the weighted indicators for each normalized indicators, respectively. For this we have to follow the same steps as above: Open the Field Calculator by clicking on the button , create a new field with the suffix “_weighted” and in the expression field.

"LandD_clas_norm" * 0.75

Fig. 152 Add new field to weight indicators

• For each indicator we now have the normalized and weighted version:

Fig. 153 Attribute Table with “_norm” and “_weighted” indicators

4. Vulnerability Score / Index

We are now ready to calculate the vulnerability score for each distrcit:

• Open the attribute table -> open the Field Calculator and create a new field with the name “vulnerability_score” and field type “Decimal Numnber (real)”. In the expression window sum up all weighted indicator values:

("LandD_clas_weigthed"  +  "perc_elderly_weighted"  +  "affunderfive_weighted") / 3 

5. Prepare Risk Assessment

In order to calculate the risk we have to bring our 3 dimension exposure, vulnerability and coping capacity together.

• Right click om one of the layer and select Properties -> Go theJoins tab

• Click on the + button, add a new join and select the layer you want to join. Define “admin2Name” as Join Field:

Fig. 154 Join Layers by Join Field

• Right click on the layer -> Export -> Save feature as and save the layer as “risk” layer into your temp folder.

• We will now work with the “risk” layer: Delete all fields but the normalized scores: Open the Attribute Table of your risk layer Toggle editing mode -> Delete field and select all the indicator fields. In the end your layer look should like this:

Fig. 155 Risk Layer Attribute Table normalized Scores

6. Risk Calculation

Finally, the risk is calculated by the geometric mean of the dimensions Exposure and Susceptibility, while Susceptibility is defined by the geometric mean of Vulnerability and the Lack of Coping Capacity. The geometric mean is chosen since it offers the advantage of rewarding balanced developments and equal reduction of deficits at all levels of the model:

\( susceptibility = \sqrt vulnerability \times lack\ of\ coping\ capacity \)

\( risk= \sqrt exposure \times susceptibility \)

• Open the Attribute table -> Field Calculator and create a field “Susceptibility” and type in the formula. Do the same creating a field named “risk” and the respective expression.

sqrt("Susceptibility" * "exposure_norm")

Fig. 156 Risk Calculation

Note

The geometric mean is a specific type of average that is calculated by multiplying together all the values in a dataset and then taking the nth root of the product, where n is the number of values. For two values, the geometric mean is the square root of their product. For three values, it’s the cube root, and so on.

6. Visualization of the Results

• Right cklick on the “risk” layer -> Properties -> Symbology

• In the down left corner click on Style -> Load Style

• In the new window click on the three points . Navigate to the “Map Template” folder and select the file “somalia_risk_assessment_style.qml”.

• Click Open. Then click on Load Style

• Back in the “Layer Properties” Window click Apply and OK

Print Layout:

• Open a new print layout by clicking on Project -> New Print Layout -> enter the name of your current Project e.g “2024_01”.

• Go the the Ex_Part_2 folder and drag and drop the file maps_somalia_template_risk_assessment.qpt in the print layout

• Change the date to the current date by clicking on “Further map info…” in the items panel. Click on the Item Properties tab and scroll down. Here you can change the date in the Main Properties field.

• If necessary, adjust the lgend by clicking on the legend in the Item Properties tab and scroll down until you see the Legend items field. If it is not there check if you have to open the dropdown. Make sure Auto update is not checked.

• Remove all itemes in the legend be clicking on the item and then on the red minus icon below.

Fig. 157 Possible Map Result

7. Automatization of the Process

HeiGIT has developed a a QGIS Risk Assessment Plugin in order to simplify this process and safe time. You can find more information about the risk methodology and the usage of the plugin here.

In order to try out the plugin and see the result, use the provided input data in your folder: “Modul_5_Ex1_Part_2\Input data\QGIS Plugin Risk Assessment\input”